Next-Gen Service Function Chain Deployment: Combining Multi-Objective Optimization With AI Large Language Models
Yuanfeng Li, Qi Zhang, Haipeng Yao, Ran Gao, Xiangjun Xin, Mohsen Guizani
Abstract
With the rapid development of next-generation mobile network services, there is a growing need for customized services to meet the demands of various network functions. Leveraging the Software-Defined Networking (SDN) architecture, Network Function Virtualization (NFV) enhances service delivery flexibility by virtualizing network appliances. This allows for Service Function Chain (SFC), which further enhances service delivery flexibility through centralized, programmable management. However, existing works require manual adjustments and tuning when adapting to evolving user demands and network expansions, lacking the flexibility needed for changing network conditions. With the rise of Large Language Models (LLMs), the automation of network management has gained new momentum by understanding programming logic, generating code, and incorporating advanced knowledge of network and optimization. This paper introduces an LLM-assisted network operating system framework and presents a case for LLM-assisted SFC optimization. Finally, it proposes an NSGA2-based multi-objective LLM optimization algorithm, which continuously updates the heuristic code policies through evolutionary iterations. Simulation results validate the effectiveness of this approach in achieving stable and efficient multi-objective optimization for SFC deployment.